Advancements in the estimation of the state of charge of lithium-ion battery: a comprehensive review of traditional and deep learning approaches
Yunhao Wu, Dongxin Bai, Kai Zhang, Yong Li, Fuqian Yang
Abstract
Accurately estimating the state of charge (SOC) of lithium-ion batteries is essential for optimizing battery management systems in various applications such as electric vehicles and renewable energy storage. This study explores advancements in data-driven approaches for SOC estimation, focusing on both conventional machine learning and deep learning techniques. While traditional machine learning methods offer reliable performance, they often encounter challenges with high-dimensional data and adaption to complex operational conditions. In contrast, deep learning models provide enhanced capabilities in nonlinear modeling and automated feature extraction, leading to improved accuracy and robustness. Through comprehensive evaluations across diverse scenarios, this research identifies key technical challenges and outlines future directions, including distributed training, incorporation of physical data, development of dynamic neural networks, and the establishment of standardized benchmarking protocols. These insights aim to guide the creation of more precise, efficient, and adaptive SOC estimation models, thereby advancing the reliability and effectiveness of battery management systems.